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BACKGROUND: Mechanical thrombectomy (MT) has revolutionized the treatment of acute ischemic stroke (AIS) due to large vessel occlusion (LVO), but its efficacy and safety in medium vessel occlusion (MeVO) remain less explored. This multicenter, retrospective study aims to investigate the incidence and clinical outcomes of vessel perforations (confirmed by extravasation during an angiographic series) during MT for AIS caused by MeVO. METHODS: Data were collected from 37 academic centers across North America, Asia, and Europe between September 2017 and July 2021. A total of 1373 AIS patients with MeVO underwent MT. Baseline characteristics, procedural details, and clinical outcomes were analyzed. RESULTS: The incidence of vessel perforation was 4.8% (66/1373). Notably, our analysis indicates variations in perforation rates across different arterial segments: 8.9% in M3 segments, 4.3% in M2 segments, and 8.3% in A2 segments (p = 0.612). Patients with perforation had significantly worse outcomes, with lower rates of favorable angiographic outcomes (TICI 2c-3: 23% vs 58.9%, p

Adolescent idiopathic scoliosis (AIS), a sideways curvature of the spine, is sexually dimorphic, with increased incidence in females. A genome-wide association study identified a female-specific AIS susceptibility locus near the PAX1 gene. Here, we use mouse enhancer assays, three mouse enhancer knockouts, and subsequent phenotypic analyses to characterize this region. Using mouse enhancer assays, we characterize a sequence, PEC7, which overlaps the AIS-associated variant, and find it to be active in the tail tip and intervertebral disc. Removal of PEC7 or Xe1, a known sclerotome enhancer nearby, or deletion of both sequences lead to a kinky tail phenotype only in the Xe1 and combined (Xe1+PEC7) knockouts, with only the latter showing a female sex dimorphic phenotype. Extensive phenotypic characterization of these mouse lines implicates several differentially expressed genes and estrogen signaling in the sex dimorphic bias. In summary, our work functionally characterizes an AIS-associated locus and dissects the mechanism for its sexual dimorphism.

Due to the rapid economic development of modern society, the demand for cargo in the shipping industry has experienced unprecedented growth in recent years. The introduction of a large number of ships, especially large, new, and intelligent ships, has made shipping networks more complex. Controlling transportation risks has become more challenging than ever before. Ship trajectory prediction based on automatic identification system (AIS) data can effectively help identify abnormal ship behaviors and reduce maritime risks such as collisions, grounding, and contacts. In recent years, with the rapid development of deep learning theories, recurrent neural network models (long short-term memory and gated recurrent unit) have been widely used in ship trajectory prediction due to their powerful ability to capture hidden information in time-series data. However, these models struggle with tasks involving high complexity of trajectory features. To address this issue, this paper introduces a switching-input mechanism based on LSTM, constructing a ship trajectory prediction model based on the SI-LSTM model. The switching-input mechanism enables the model to adjust its processing of important information according to dynamic changes in input data, effectively capturing local features of complex trajectories. The experimental section, which includes eight cases of complex trajectories, demonstrates the competitive generalization ability and prediction accuracy of SI-LSTM. [ABSTRACT FROM AUTHOR]

Ships are technologies of maritime mobility. But sometimes ships are immobile—they stop and remain stationary for short or prolonged times. A degree of stasis inside and outside ports is both usual and essential for facilitating the movement of ships in global markets. This paper makes two important points: first, echoing existing literature, frequent stationarity (or waiting) is a normal occurrence in the industry. Second, it is not just mobilities that are disrupted via moments of stasis; immobilities themselves have distinct patterns that too can be disrupted. This nuance is vital: there is a need to understand the disruptions to immobilities rather than understanding immobilities as disruptions to the general condition of mobility, both within—and beyond—the shipping example. We argue that understanding disruptions to immobilities is vital to grasping the dynamics of shipping, alongside other (im)mobilites (car, train, plane), their conditions, and the politics that shape our world on the move. Using a data-driven approach, embracing AIS methods for exploring ship stationarity around US waters during the COVID-19 pandemic, this paper upends the assumptions of ship (im)mobilities through the example of wait times, calling for scholars to give space and time to everyday immobilities and their disruptions too. [ABSTRACT FROM AUTHOR]

Achieving accurate and efficient ship-type recognition is crucial for the development and management of modern maritime traffic systems. To overcome the limitations of existing methods that rely solely on AIS time series data or navigation trajectory images as single-modal approaches, this study introduces TrackAISNet, a multimodal ship classification model that seamlessly integrates ship trajectory images with AIS time series data for improved performance. The model employs a parallel structure, utilizing a lightweight neural network to extract features from trajectory images, and a specially designed TCN-GA (Temporal Convolutional Network with Global Attention) to capture the temporal dependencies and long-range relationships in the AIS time series data. The extracted image features and temporal features are then fused, and the combined features are fed into a classification network for final classification. We conducted experiments on a self-constructed dataset of variable-length AIS time series data comprising four types of ships. The results show that the proposed model achieved an accuracy of 81.38%, recall of 81.11%, precision of 80.95%, and an F1 score of 81.38%, outperforming the benchmark single-modal algorithms. Additionally, on a publicly available dataset containing three types of fishing vessel operations, the model demonstrated improvements in accuracy, recall, and F1 scores by 5.5%, 4.88%, and 5.88%, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explores the effectiveness of a CNN-Attention model using AIS vessel position data and fishing log data to predict fishing grounds for Argentine squid (Illex argentinus) in the Southwest Atlantic high seas. Traditional forecasts primarily rely on fishing log data, which suffer from delays and limited spatial resolution, failing to meet actual fishing needs. This research utilizes vessel position data with a spatial resolution of 0.1° × 0.1° and monthly temporal resolution, creating a more refined dataset. Compared to fishing log datasets that include marine environmental factors (like SST, SSH, and MLD), this new dataset offers higher timeliness and accuracy. The analysis revealed that, from January to April, both datasets yielded good predictions, but the model based on vessel position data performed poorly in December, May, and June compared to the fishing log model. This research not only provides a reliable forecasting method for Argentine squid fishing grounds but also offers insights for future marine habitat predictions and ecological protection. By integrating AIS vessel data and fishing logs, the study proposes an effective strategy to enhance forecast accuracy, supporting efforts against climate change and illegal fishing. To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas. [ABSTRACT FROM AUTHOR]

Fleet productivity is an important element in shipping economics, a defining factor in the asset's profitability, and a key driver of the emission footprint of the industry. This paper analyzes the productivity of bulk vessels in a multi-dimensional framework by utilizing over 800,000 voyages data which were carried out by 17,764 vessels over a 7-year horizon and was derived from the Automated Identification System. An examination of the underlying factors driving transportation output provides further understanding of how the operational behaviours impact vessel productivity. The insights from this research have important implications for policy making in investment, operational enhancement, and emission reduction. [ABSTRACT FROM AUTHOR]

This study aimed to validate the predictive performance of ASTRAL and THRIVE scales when used for patients aged 60 years and older with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT). All enrolled patients received IVT therapy. The enrolled patients were divided into two groups in accordance with the modified Rankin scale(mRS) score at the time of discharge: good-outcome (mRS ≤ 2) and poor-outcome (mRS ≥ 3) groups. The receiver operating characteristic (ROC) curve was plotted using MedCalc software, the area under the ROC curve (AUC) was calculated. The Delong test was used to compare the predictive performance of ASTRAL and THRIVE scales, with P < 0.05 being considered a statistically significant difference. The AUCs of ASTRAL and THRIVE in predicting poor outcomes after thrombolysis in elderly patients with AIS were 0.771 and 0.701, respectively. The difference in AUC between ASTRAL and THRIVE was 0.070, and a statistically significant difference (P < 0.05) was found. ASTRAL's predictive performance was better than that of THRIVE. ASTRAL is a reliable predictive tool for assessing the poor outcome of IVT therapy for elderly patients aged ≥ 60 years with AIS. [ABSTRACT FROM AUTHOR]

Objectives: Adolescent idiopathic scoliosis (AIS) is a 3‐dimensional spinal deformity involving lateral curvature, sagittal plane imbalance, and vertebral rotation. In China, AIS affects over 3 million individuals, with 300,000 new cases annually. AIS impacts physical and psychological well‐being, necessitating tailored treatment plans based on growth risk factors. This study evaluates the consistency of ultrasound and X‐ray assessments of the Risser sign in AIS patients and explores correlations between iliac crest distance and pelvic rotation degree. Methods: This prospective study enrolled 80 patients diagnosed with AIS from June to September 2023 at Changzhou Sports Hospital. Eligible participants were aged 10–18 with a Cobb angle >10°. Ultrasonographic examinations were conducted by 3 experienced physicians using the VINNO V10 portable ultrasound system. The primary outcome was the Risser stage determined by X‐ray and ultrasound, with secondary outcomes including thoracic and thoracic‐lumbar segment rotation angles. Statistical analyses included kappa statistics, correlation analyses, and multiple regression. Results: Among the 77 valid cases, 154 iliac wings were evaluated, with a high concordance rate of 77% between ultrasound and X‐ray assessments. kappa values for left and right iliac crests were 0.723 and 0.808, respectively. Grouping Risser grades (0–1, 2–3, and 4–5) into 3 categories yielded kappa values of 0.93, 0.96, and 0.93, indicating high consistency. Significant correlations were found between iliac crest distances and rotation angles (left iliac crest distance and left thoracic rotation angle, r = 0.56, P <.001; right iliac crest distance and right thoracic‐lumbar rotation angle, r = 0.69, P <.001; right iliac crest distance and right thoracic rotation angle, r = 0.39, P <.01). Conclusions: Ultrasound is a reliable, radiation‐free alternative to X‐ray for assessing the Risser sign in AIS patients. Despite observed inconsistencies in intermediate Risser grades, ultrasound's ability to reduce radiation exposure and provide consistent results makes it a valuable tool in clinical practice. Further research is needed to optimize ultrasound techniques and explore its potential for early detection and intervention in scoliosis management. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic transformation (HT) is a tragic complication of acute ischemic stroke (AIS), with spontaneous HT (sHT) occurring even without reperfusion therapies. Despite evidence suggesting that several inflammation biomarkers are closely related to HT, its utility in sHT risk stratification remains unclear. This study aimed to identify and integrate effective inflammatory biomarkers associated with sHT and to develop a novel nomogram model for the early detection of sHT. Methods: We conducted a retrospective observational cohort study of AIS patients receiving conventional medical treatment solely from March 2022 to March 2023, using a prospectively maintained database. All patients underwent CT follow‐up within 7 days after admission, with sHT occurrence within this period as the outcome. Data on demographics, clinical information, laboratory results, and imaging were collected. The cohort was divided into training and validation sets (7:3). Least absolute shrinkage and selection operator (LASSO) regression selected inflammatory biomarkers for a novel index. Univariable and multivariable logistic regressions were conducted to identify independent sHT risk factors. Receiver operating characteristic (ROC) analysis determined optimal cut‐off values for continuous factors. A nomogram was developed and validated internally and externally. Predictive accuracy was assessed using the area under the ROC curve (AUC) and calibration plots. Decision curve analysis (DCA) evaluated clinical usefulness. Results: Of 803 AIS patients, 325 were included in the final analysis. sHT was found in 9.5% (31 patients). Training (n = 228) and validation (n = 97) cohorts showed no significant demographic or clinical differences. LASSO regression integrated neutrophil‐to‐albumin ratio (NAR) and triglycerides (TGs) into a novel index—NATG. Independent sHT risk factors included baseline National Institute of Health Stroke Scale (NIHSS) (OR = 1.09, 95% CI (1.02, 1.16), p = 0.0095), NATG (OR = 1534.87, 95% CI (5.02, 469638.44), p = 0.0120), D‐dimer (DD) (OR = 1.12, 95% CI (1.01, 1.25), p = 0.0249), and total cholesterol (TC) (OR = 1.01, 95% CI (1.00, 1.01), p = 0.0280), with their respective optimal cut‐off values being 13, 0.059, 0.86, and 3.6. These factors were used to develop the nomogram in the training cohort, which achieved an AUC of 0.804 (95% CI, 0.643–0.918) in the training cohort and 0.713 (95% CI, 0.499–0.868) in the validation cohort, demonstrating consistent calibration. DCA confirmed the nomogram's clinical applicability in both cohorts. Conclusions: A novel indicator combining NAR and TG is positively associated with sHT in AIS patients. The constructed nomogram, integrating this novel indicator with other risk factors, provides a valuable tool for identifying sHT risk, aiding in clinical decision‐making. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective analysis of a longitudinal cohort. Objective. To identify the presence of scoliosis from surface data. Summary of Background Data. Identifying AIS can be difficult. Screening is not universal for reasons including high false positive and negative rates. These difficulties can lead to some adolescents missing out on bracing. Methods. Logistic regression analysis of ISIS2 surface topography images only was performed. The x,y positions of the shoulders (Sh), axillae (Ax), waist (waist) and the x,y,z positions of the most prominent points over the posterior torso (scap) were used for the thoracic, thoracolumbar/lumbar and whole spine. The models were used to identify the presence of a 20-degree or larger scoliosis. Differences in the position of the landmarks were analyzed comparing left and right, with the suffix "Ht" representing a difference in the y coordinate, "Off" the x coordinate, and "Depth," the z coordinate. Model accuracy was assessed as both percentages and ROC curves with the coefficients as odds ratios. Results. There were 1283 images (1015 females and 268 males) all with a diagnosis of AIS. The models identified scoliosis in the thoracic spine with an 83% accuracy (AUC 0.91), thoracolumbar/ lumbar spine with 74% accuracy (AUC 0.76), and whole spine with 80% accuracy (AUC 0.88). Significant parameters were Ax-DiffHt, AxDiffOff, WaistDiffHt, ScapDiffOff, and ScapDiffHt for the thoracic curves, AxDiffHt, AxDiffOff, WaistDiffHt for the thoracolumbar/lumbar curves, and AxDiffHt, AxDiffOff, Waist-DiffHt and ScapDiffHt for the whole spine. Conclusions. The use of fixed anatomical points around the torso, analyzed using logistic regression, has a high accuracy for identifying curves in the thoracic, thoracolumbar/lumbar, and whole spines. While coming from surface topography images, the results raise the future use of digital photography as a tool for the identification of small scoliosis without using other imaging techniques. [ABSTRACT FROM AUTHOR]

In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS stability. To address the challenges of establishing consistent navigation and improving trajectory quality, this study introduces a novel method to directly identify AIS-matched trajectories from radar plots. This approach treats radar points as probability clouds, generating a multi-dimensional information layer by stacking these clouds after differential transformations based on AIS data. The resulting layer undergoes filtering and clustering to extract point sets that align with AIS data, effectively isolating matching trajectories. The algorithm, validated with simulated data, rapidly identifies target trajectories amid extensive interference without requiring strict parameter adjustments. In measured data, the algorithm rapidly provides matching trajectories, although further human judgment is still required due to the potential absence of true values in measured data. [ABSTRACT FROM AUTHOR]

Background: Orthotic treatment is a well-acknowledged conservative treatment for moderate adolescent idiopathic scoliosis (AIS). The efficacy of this treatment is significantly determined by the forces applied to the bodies of patients. However, there is uncertainty regarding the optimal force levels that should be applied to the patient's torso by spinal orthosis. This study aims to identify reference values for the controlling forces in AIS management. Methods: A comprehensive literature search was performed in five databases (PubMed, Scopus, Cochrane Library, CINAHL, and Web of Science). Only studies written in English and covering the force/pressure measurements of spinal orthosis for the treatment of AIS were included, without publication date restrictions. The methodological index for non-randomized studies (MINORS) was employed for the methodological quality assessment, and force measurements were standardized to pressure in kilopascals (kPa) for comparison. Results: From the initial 10,452 records, 10 studies were admitted for the final analysis. All the included studies reported the interface pressure between the thoracic (T) pad and patient's trunk, and seven studies evaluated the pressure from the thoracolumbar/lumbar (TL/L) pad. These studies used different pressure sensors or transducers with the range from 5.6 to 82.5 kPa for the T pads, and 4.8 to 85.1 kPa for the TL/L pads. Four studies reported strap tensions of 26.8 to 60.4 N. Higher strap tension was correlated with increased interface pressure (r = 0.84). Conclusion: The mean strap tension was 42.5 N, the median interface pressure of the T pads was 8.75 kPa, and the median pressure of TL/L regions was 7.11 kPa without the outliers. The findings provide a baseline value for designing adjustable straps and strategically distributing pressure in orthoses. [ABSTRACT FROM AUTHOR]

This study examines the development of a machine-learning model to forecast weekly throughputs of dry bulk cargo in the short term based on automatic identification system (AIS) data. Specifically, the weekly amounts of iron ore exported from several major ports in Australia and Brazil in the latter half of 2019 are forecasted three weeks in advance using a long short-term memory model. We examine many variables extracted from AIS data, including the vessel position, speed, draught, and destination, as the input features of the model. Consequently, we develop a highly accurate forecasting model that uses four influential variables derived from AIS data, namely, vessel traffic around the target port and in the region, vessel traffic at major partner import ports, and vessel traffic at the target port during the past year. Finally, by forecasting the weekly port cargo throughputs in the first half of 2020, which was affected by the COVID-19 pandemic, the applicability of the model is confirmed, even for ports where the throughput fluctuates significantly. In particular, this study demonstrates that AIS data are beneficial not only as a real-time traffic database but also as a database containing various related explanatory variables, including historical vessel traffic. [ABSTRACT FROM AUTHOR]

The Arctic region is undergoing significant changes in maritime accessibility. This study investigates observed ship trajectories from 2013 to 2020 to demonstrate the recent trends of Arctic traffic. A notable surge in maritime activities has been observed, particularly during summer months, driven by economic interests and the increasing popularity of existing routes. Unique patterns in the northern Barents Sea have been observed where ships favour different routes based on seasonal ice conditions. Another contribution from this work is the validation of the Arctic Traffic Accessibility Model (ATAM) using the observed ship traffic data. Results show that the ATAM model underestimates the accessibility and vessel travel speed. This is largely due to outdated model parameters. The predefined ice multipliers and calculation of ice numerals used in the ATAM may not accurately reflect real-world conditions. [ABSTRACT FROM AUTHOR]

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth's curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions. [ABSTRACT FROM AUTHOR]

Tadeusz Ka&lstrok;u&zdot;ewski,1,2 Iwona Pinkier,1 Urszula Wysocka,1 Jordan Sa&lstrok;amunia,2 &Lstrok;ukasz K&eogon;pczy&nacute;ski,1,2 Ma&lstrok;gorzata Piotrowicz,1 Bogdan Ka&lstrok;u&zdot;ewski,2 Agnieszka Gach1 1Department of Genetics, Polish Mother’s Memorial Hospital Research Institute, Lodz, 93-338, Poland; 2R&D Division, Laboratory of Medical Genetics, GENOS Sp. z o.o., Lodz, 91-033, PolandCorrespondence: Tadeusz Ka&lstrok;u&zdot;ewski, Email tadeusz.kaluzewski@iczmp.edu.plAbstract: Androgen insensitivity syndrome (AIS) is an X-linked genetic disorder caused by mutations in the androgen receptor gene (AR), leading to impaired androgen signaling and resulting in varying degrees of undermasculinization in individuals with a 46,XY karyotype. This study aimed to expand the molecular landscape of AIS by identifying and characterizing pathogenic variants in the AR gene via next-generation sequencing (NGS). Molecular diagnostics revealed eight distinct variants within the AR gene, two of which had not been previously described. These include the following novel variants: c.3G>A, and c.1344_1345insTA. This study broadens the spectrum of known AR gene mutations associated with AIS and highlights the critical role of molecular diagnostics in the accurate classification of variants. These findings will aid in enhancing the clinical management and genetic counseling of individuals affected by AIS.Keywords: androgen insensitivity syndrome, AIS, androgen receptor gene, AR gene, next-generation sequencing, NGS, disorders of sex development, complete androgen insensitivity syndrome, CAIS

Abstract Adolescent idiopathic scoliosis (AIS) is the most common form of spinal deformity in the younger population. The surgical management for these patients improved constantly over the last year and might not be comparable to modern treatment strategies. However, under this aspect the present investigation updates and discusses current evidence regarding the long-term outcome of the surgical management of AIS. All the clinical studies which evaluated the long-term outcomes of spinal fusion were considered. Level of evidence, clinical and radiological data, results of health-related questionnaires and surgery-associated complications during long-term follow-up, e.g., proximal and distal junctional kyphosis (PJK/DJK), and adjacent segment degeneration (ASD), are presented. Data concerning the following patient-reported outcomes measures were collected: Oswestry Disability Index (ODI), Scoliosis Research Society (SRS) Outcome Questionnaire, visual analogue scale (VAS), and short form-12 and 36 (SF-12/SF-36). Overall, data from 1115 patients were included. Of them, 324 underwent anterior and 791 posterior spinal fusion. One study focuses on a combined anterior/posterior fusions. The mean follow-up was 22.6 years (posterior fusion: 24.6 years, anterior fusion: 18.31 years). Seven studies focus on the thoracic segments, while 12 focus on the lumbar spine. Data on imaging was reported in 13 studies and those on PROMs in 15 investigations. In conclusion, there is low quality and paucity of long-term data on AIS. However, the long-term results of the implicated studies on AIS patients in this review appear to be satisfactory, although there are limitations in the outcome compared to healthy comparison cohorts. Adjacent degenerations appear to be the most common mechanical complication after long-segment fusions, despite their influence on the outcome remains unclear. With regard to pregnancies, there are slightly increased cesarean section rates, which could be explained by deviations in the sagittal profile.

Acute ischemic stroke (AIS) occupies one of the leading places in the structure of disability and mortality in adult population throughout the world, which causes a significant social and economic burden of this pathology. Recent advances in AIS diagnosis and treatment have significantly reduced mortality and improved functional outcomes in patients. However, categories of patients with unsatisfactory results and complications of reperfusion therapy still remain. In some of them, mortality may exceed the overall rate. To this day, there are controversial aspects in determining indications and choosing the preferred method of cerebral reperfusion. The presented cases describe the results of using thrombolytic therapy and thromboextraction in AIS patients of different age groups. The importance of various neuroimaging methods (multislice computed tomography, computed angiography, duplex ultrasound of extra- and intracranial arteries) for timely diagnosis and determination of optimal revascularization tactics was noted.

Abstract Background Brace therapy’s influence on adolescent idiopathic scoliosis’s (AIS) natural course is inconclusive. Methods Brace-treated AIS patients from 2016 to 2020 were examined regarding four endpoints at brace weaning: surgery need, curve progress ≥ 6° and > 45°, and curve improvement ≥ 6°. Prediction variables’ influence was computed for the all-patients group and three subgroups (Subgroup 1: fulfilling the Scoliosis Research Society’s [SRS] criteria, Subgroup 2: initial Cobb angle 40°). According to the data characteristics, parametric and non-parametric tests and binary logistic regression were performed. Results The research included 69 patients. Overall, after brace weaning surgery was recommended for 20.3% of them, curve progression was ≥ 6° in 23.2%, the Cobb angle was beyond 45° in 11.6%, and the Cobb angle improved by ≥ 6° in 20.3%. Patients needing surgery had a significantly higher initial Cobb angle (38.8° ± 9.8° vs 27.8° ± 7.6°, p 40°. Real brace wear (RBW) in all groups had no significant effect on outcome. Two significant subgroup differences were found: Surgery recommendation and curve progression beyond 45° were significantly more frequent in the initial Cobb angle > 40° subgroup. Conclusions Brace effectiveness can be assumed because curve improvement was achieved in nearly one-fifth with sufficient in-brace correction. However, no significant influence of RBW on the outcome endpoints was demonstrated. To clarify conflicting results, big data management, including untreated patients, must be employed to further research AIS’s multifactorial influenced aetiology and course. Meanwhile, it is worth starting bracing in AIS in practice also beyond the SRS’s criteria.

Abstract Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.

Suwen Huang,1,&ast; Wei Xie,1,2,&ast; Yufan Gao,1,3,&ast; Yining Jin,1,3 Yilin Chen,1,2 Guoliang Zhou,1,2 Feng Chen,1,2 Qiaoqiao Jin,1,2 Zihao Wu,1,2 Lingsheng Wang,1,2 Chao Chen,4 Yiyun Weng,1 Guangyong Chen5 1Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, People’s Republic of China; 3The Second School of Medicine, Wenzhou Medical University, Wenzhou, People’s Republic of China; 4Department of Nutriology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 5Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guangyong Chen, Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China, Email gychen@wmu.edu.cn Yiyun Weng, Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China, Email wengyiyun2012@126.comBackground and Purpose: The dynamic systemic inflammation level and stroke-associated infection (SAI) are related to the prognosis of acute ischemic stroke (AIS). We aimed to explore whether the systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and their dynamic changes possess predictability for SAI and long-term prognosis.Methods: A total of 1804 AIS patients without intravenous thrombolysis in two hospitals were included. We explored the relationship between SIRI, SII, and their dynamic changes and outcomes by constructing clusters. The mediating effects of SAI between prognosis and systemic inflammation were further evaluated.Results: Each SD increase in the concentration of SIRI exhibited a significant correlation with the risk of poor functional outcome, mortality, and functional dependency. Through K-means clustering analysis, patients with dramatically elevated or decreased systemic inflammation levels of SIRI (OR: 2.293, 95% CI: 1.279– 4.109) and SII (OR: 3.165, 95% CI: 1.627– 6.156) within 7 days had a higher risk of functional outcome. Through mediation analysis, SAI mediated the association between systemic inflammation and poor prognosis (SIRI: 33.73%, SII: 16.01%).Conclusion: Dramatically changing dynamics of SIRI and SII were significantly associated with a higher risk of poor prognosis in AIS patients. SAI mediated the association between systemic inflammation and prognosis at 1 year.Keywords: AIS, inflammation, SAI, mediation analysis, clustering analysis

Pulmonary function in patients with scoliosis has been a topic of concern, with some reports of markedly decreased ventilatory function leading to disability and increased mortality in patients with severe scoliosis. Only limited data is available concerning pulmonary function in adult patients with scoliosis. To report the long-term pulmonary function (PF) in patients diagnosed with idiopathic scoliosis (IS) compared with an age-matched population using extended pulmonary function testing (EPFT). Retrospective clinical follow-up. A total of 177 patients seen at our institution from 1972 to 1983 for a pediatric spinal deformity were assessed for inclusion in the study. About 77/129 eligible patients with IS (60%) partook in a clinical examination including radiographs, and EPFT. The EPFT values included forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC ratio, vital capacity (VC), total lung capacity (TLC), residual volume (RV), RV/TLC ratio, diffusion capacity of carbon monoxide (DLco), carbon monoxide transfer coefficient (KCO) and alveolar volume (VA). Results were expressed with z-scores derived from height and arm span normative data. Z-scores were calculated as z-score=Measured PF-Predicted PF /Relative standard deviation (RSD). The limits of normal, are defined as the 5th and 95th percentile limits (z-score between −1.645 and 1.645), respectively. Patients underwent a clinical examination with full spine standing radiographs and EFPT. The results were compared between patients with thoracic and thoracolumbar/lumbar (TL/L) main curves, and overall compared with a background population. Results were expressed with z-scores derived from height and arm span normative data. Of 77 included patients, 76 (99%) were females with a mean age of 54.6±2.5 years. The mean follow-up time was 40.8±2.8 years. Forty-four patients had thoracic main curves, and 33 had TL/L main curves. We found no pulmonary impairment based on z-scores in the total cohort or between groups, with only patients who were current or previous smokers, having z-scores below the normal limits Patients with main thoracic curves displayed significantly lower PF on mean absolute values and mean z-scores on FEV1, FVC, FEV1/FVC ratio, VC, TLC, and DLco compared with main TL/L curves. Patients with thoracic curves had significantly larger Cobb angles at follow-up; 52±17° compared with 40±22° (p-value <.05) in the TL/L group. We found no linear association between thoracic Cobb angle and degree of pulmonary impairment assessed with DLco, TLC, and FVC. Comparison of pulmonary z-scores based on arm span data, differed significantly on FVC and TLC, with the arm span measurements showing lower mean z-scores (p-value <.05). Using EPFT, no pulmonary impairment could be demonstrated compared to the age-matched population 40 years after a diagnosis of IS. However, patients with thoracic curves had decreased PF compared to patients with TL/L curves although within the normal range. Thus, when treated as current guidelines suggest, patients with idiopathic scoliosis can expect the same long-term pulmonary function as the general population. [ABSTRACT FROM AUTHOR]

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6 × 10 − 4 compared with the baseline model. [ABSTRACT FROM AUTHOR]

This research paper presents an effective approach to reducing marine pollution and costs by determining the optimal marine alternative fuels framework for short-sea shipping vessels, with a focus on energy efficiency. Employing mathematical models in a Python environment, the analyses are tailored specifically for conventional and fully autonomous high-speed passenger ferries (HSPFs) and tugboats, utilizing bottom-up methodologies, ship operating phases, and the global warming potential approach. The study aims to identify the optimal marine fuel that offers the highest Net Present Value (NPV) and minimal emissions, aligning with International Maritime Organization (IMO) regulations and environmental objectives. Data from the ship's Automatic Identification System (AIS), along with specifications and port information, were integrated to assess power, energy, and fuel consumption, incorporating parameters of proposed marine alternative fuels. This study examines key performance indicators (KPIs) for marine alternative fuels used in both conventional and autonomous vessels, specifically analyzing total mass emission rate (TMER), total global warming potential (TGWP), total environmental impact (TEI), total environmental damage cost (TEDC), and NPV. The results show that hydrogen (H2-Ren, H2-F) fuels and electric options produce zero emissions, while traditional fuels like HFO and MDO exhibit the highest TMER. Sensitivity and stochastic analyses identify critical input variables affecting NPV, such as fuel costs, emission costs, and vessel speed. Findings indicate that LNG consistently yields the highest NPV, particularly for autonomous vessels, suggesting economic advantages and reduced emissions. These insights are crucial for optimizing fuel selection and operational strategies in marine transportation and offer valuable guidance for decision-making and investment in the marine sector, ensuring regulatory compliance and environmental sustainability. [ABSTRACT FROM AUTHOR]

Ecosystem-based fisheries management requires high-precision fisheries information to provide relevant data for natural resource management, assessment, and marine spatial planning. This study utilizes Automatic Identification System (AIS) data from light purse seine vessels from the Chinese mainland that were collected from May to November between 2020 and 2022, along with the corresponding environmental data. By applying boosted regression trees (BRTs) and generalized additive models (GAMs), this study establishes nonlinear relationships between fishing intensity and predictor variables and explores the ecological and environmental drivers behind the spatial distribution of light purse seine vessels from the Chinese mainland in the Northwest Pacific. This research identifies the key influencing factors and reveals significant seasonal preferences for different marine environments in various months, with chlorophyll-a being the primary influencing factor. The predicted fishing effort closely resembles observed data, providing valuable information to support fisheries resource management and planning. [ABSTRACT FROM AUTHOR]

Background: Brace therapy's influence on adolescent idiopathic scoliosis's (AIS) natural course is inconclusive. Methods: Brace-treated AIS patients from 2016 to 2020 were examined regarding four endpoints at brace weaning: surgery need, curve progress ≥ 6° and > 45°, and curve improvement ≥ 6°. Prediction variables' influence was computed for the all-patients group and three subgroups (Subgroup 1: fulfilling the Scoliosis Research Society's [SRS] criteria, Subgroup 2: initial Cobb angle < 25°, Subgroup 3: initial Cobb angle > 40°). According to the data characteristics, parametric and non-parametric tests and binary logistic regression were performed. Results: The research included 69 patients. Overall, after brace weaning surgery was recommended for 20.3% of them, curve progression was ≥ 6° in 23.2%, the Cobb angle was beyond 45° in 11.6%, and the Cobb angle improved by ≥ 6° in 20.3%. Patients needing surgery had a significantly higher initial Cobb angle (38.8° ± 9.8° vs 27.8° ± 7.6°, p < 0.001), lower Risser stages (p = 0.010), and higher Nash and Moe degrees (p = 0.030). Patients with curve progress ≥ 6° were younger at first curve notification (12.4 ± 1.5 vs 13.7 ± 1.7 years, p = 0.011) and older at menarche (13.4 ± 1.1 vs 12.6 ± 1.2 years, p = 0.037). Furthermore, 21.6% of all Risser 3 and 4 patients had still curve progress ≥ 6°. In-brace correction was significantly higher in patients with curve improvement ≥ 6° vs < 6° (54.0% ± 31.2% vs 31.9% ± 30.7%; p = 0.019). Nine patients fulfilled the SRS criteria, 22 had initial Cobb angles < 25°, and 11 had > 40°. Real brace wear (RBW) in all groups had no significant effect on outcome. Two significant subgroup differences were found: Surgery recommendation and curve progression beyond 45° were significantly more frequent in the initial Cobb angle > 40° subgroup. Conclusions: Brace effectiveness can be assumed because curve improvement was achieved in nearly one-fifth with sufficient in-brace correction. However, no significant influence of RBW on the outcome endpoints was demonstrated. To clarify conflicting results, big data management, including untreated patients, must be employed to further research AIS's multifactorial influenced aetiology and course. Meanwhile, it is worth starting bracing in AIS in practice also beyond the SRS's criteria. [ABSTRACT FROM AUTHOR]

Purpose: To examine if coronal deformity angular ratio (C-DAR) serves as a predictor for progression to surgical magnitude in patients with Adolescent Idiopathic Scoliosis (AIS) treated with thoracolumbar sacral orthosis (TLSO). Methods: Patients with AIS, prescribed a full-time TLSO, Cobb angle 20–40°, Risser 0–2, who wore the brace ≥ 12.9 h and reached skeletal maturity/surgery were included retrospectively. C-DAR was defined as the Cobb angle divided by the number of vertebrae in the curve, yielding a larger value in short curves. The association between C-DAR and the risk of progression to surgical magnitude (> 45°) was assessed. Secondly, we evaluated the association between pre-treatment Cobb angle and in-brace correction on the risk of progression to > 45°. Results: We included 165 patients with a mean Cobb angle of 30 ± 6°. Of these, 46/165 (28%) progressed ≥ 6° and 26/165 (16%) reached surgical magnitude at the end of treatment. C-DAR was a significant predictor for risk of progression to surgical magnitude with an OR of 1.9 (CI 1.2–2.9) per unit increase in C-DAR. A threshold value of 5.15 was established and demonstrated an OR 5.9 (CI 2.1–17.9) for curve progression to a surgical magnitude. Likewise, pre-treatment Cobb angle showed a significant OR 1.3(CI 1.2–1.4) per degree increase in Cobb, whereas in-brace % correction showed OR 0.96 (CI 0.93–0.98). Conclusion: C-DAR is an independent predictor for progression to a surgical magnitude in AIS patients treated with bracing. Patients with a higher C-DAR should be counseled to help set realistic expectations regarding the likelihood of curve progression despite compliance with brace wear. [ABSTRACT FROM AUTHOR]

Objective: To validate the predictive performance of the THRIVE, ASTRAL, and iScore scales for clinical functional outcomes following mechanical thrombectomy (MT) for acute ischemic stroke (AIS). Methods: A total of 111 patients meeting the inclusion criteria were included in this study, with 59 (53.2%) having a good prognosis and 52 (46.8%) having a poor prognosis. MedCalc software was applied to plot receiver operating characteristic (ROC) curves, calculate the area under the curve (AUC), and compare the predictive efficacy of the three scales two by two using Delong text. Statistical significance was defined as Pc < 0.05. Results: Logistic binary regression multifactorial analysis revealed that iScore is one of the poor predictors of prognosis in patients with MT. The AUC values for the THRIVE, ASTRAL, and iScore scales in predicting prognosis after MT were found to be 0.713, 0.738, and 0.820, respectively. Conclusion: The iScore is a reliable tool for assessing the poor prognosis of MT in patients with AIS. [ABSTRACT FROM AUTHOR]

Objective: In this study, the relationship between maximum carotid plaque area and stroke prognosis was analysed by carotid ultrasonography, and the relevant risk factors affecting the prognosis of acute ischaemic stroke (AIS) were investigated to provide novel insights into stroke prevention and management. Methods: A total of 205 AIS patients with carotid plaques were included in this study. Based on the mRS score at discharge, patients with AIS were classified into the good prognosis group (mRS ≤ 2) and poor prognosis group (mRS ≥ 3). SPSS 25.0 was used to analyse the data. Univariate and multivariate analyses were performed on the two groups with good and poor prognosis. Comparison between good and poor prognosis in patients with AIS in different circulatory systems was performed using the Kruskal–Wallis test. Differences were considered statistically significant at P < 0.05. Results: Comparison of baseline data revealed differences in carotid plaque diameter, carotid intima-media thickness, maximum carotid plaque area, history of previous stroke and plaque echogenicity between the good and poor prognosis groups (P < 0.05). Results of multifactorial analyses of logistic binary regression indicated that history of previous stroke and maximum carotid plaque area were predictors of poor prognosis, with odds ratio of 2.515 (95%CI [1.327–4.764]) and 1.019 (95%CI [1.006–1.032]), respectively. Conclusion: The maximum carotid plaque area and history of previous stroke are important predictors for assessing prognosis in patients with AIS. [ABSTRACT FROM AUTHOR]

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness. [ABSTRACT FROM AUTHOR]

This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges. [ABSTRACT FROM AUTHOR]

Objective: Recently, the South China Sea has been facing a crisis of depleted fishery resources, primarily due to the impacts of illegal, unreported, and unregulated fishing activities, as well as overfishing. Accurately understanding the fishing activity intensity in the South China Sea holds significant implications for the sustainable management of fisheries resources. Methods: Leveraging the automatic identification system trajectory data from 2018, this paper employs spatial statistical methods and fishing effort indicators to comparatively analyze the spatial variations in fishing intensity between Chinese and Vietnamese fishing vessels. Result: The results of this study show that (1) in 2018, the total fishing effort of Chinese fishing vessels in the South China Sea was 7.65 times that of Vietnamese vessels, but during China's South China Sea fishing moratorium, the fishing effort exerted by Vietnamese vessels surpassed that of China and (2) the top 10 ports in China and Vietnam support approximately 30% and 55.13% of their respective fishing intensities in the South China Sea. Conclusion: The study highlights significant variations in fishing intensity between Chinese and Vietnamese vessels and the substantial support provided by major ports. These findings offer valuable insights for fisheries resource monitoring and maritime spatial planning, contributing to the sustainable management of the South China Sea's fisheries resources. Impact statementThis study sheds light on fishing by Chinese and Vietnamese vessels during the South China Sea fishing ban and the intensity of fishing by vessels supported by ports along the South China Sea. By understanding these patterns, we can better manage fishery resources in the South China Sea and ensure sustainable fishing. [ABSTRACT FROM AUTHOR]

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture. [ABSTRACT FROM AUTHOR]

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect. [ABSTRACT FROM AUTHOR]

In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused severe disruptions throughout global supply chains. In response to this situation, container carriers had been cancelling services and port calls. The reasons behind the cancellations were diverse: restoring schedule reliability; coping with sudden demand decreases; or with severe port congestion. To fully understand the implications of this practice, it is crucial to have a robust measurement method. This paper presents a novel method to estimate the incidence of port call cancellations based on AIS data. A normal service is first defined, on the basis of the most frequent port call sequence, and deviations are measured subsequently. As a first glance at the unique value of this method, we apply it to the ports along the Europe-Far East route. A binomial logistic model expresses the probability a port is skipped, based on its own characteristics, the size of vessels, and the region in which the port is located. We find non-trivial effects related to vessel size. At the largest end of the vessel size scale, the ports attracting mega vessels (with a capacity above 15 K TEUs) were less affected by cancellations in 2018–2019. This relationship reversed during the 2020–2021 period, and handling mega vessels seemed to have become a burden for ports during the pandemic. Another important result of this study is that, before the COVID-19 pandemic, the rate of cancellations was much more uneven between world regions than after the COVID-19 outbreak. This study provides useful operational insights to port authorities and governments, enabling them to anticipate the effects of future crises. [ABSTRACT FROM AUTHOR]

The circular economy (CE) brings many opportunities, but also many challenges for ports, cities, and their hinterland. The goal of this paper is twofold. First, we embrace the inherent uncertainty of the spatial impact of the CE on ports and cities. We employ scenario methodology to guide us in steering this uncertainty by developing four scenarios. To explore the complexity of these four scenarios, we focus on the Dutch province of South-Holland. This region hosts the port of Rotterdam, the largest port in Europe, and its direct hinterland consists of a dense urbanised region. As such, the four scenarios cannot only shed a light on the future of the port, but also how its relations with its direct urbanized hinterland can potentially change. In two scenarios deglobalisation occurs. The consequences are, on the one hand, that the port's focus changes more to its direct hinterland instead of a global oriented focus. On the other hand, the existing water bound industrial areas in, or nearby cities increase in importance, in contrast to the contemporary pressure to redevelop these into waterfront residential and commercial areas. In other words, port and city/region grow towards each other. The second goal of the paper is to dive into the specific consequences of these scenarios for day-to-day planning practices. By combining micro-economic and AIS shipping data, we discovered the most important terminals and industrial areas for the transition towards a CE in port, city, and hinterland.